


import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_SCREAMING_SNAKE_CASE     :					Any      =       os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
 from run_translation import main  # noqa


set_seed(42)
_SCREAMING_SNAKE_CASE     :					str      =       "sshleifer/student_marian_en_ro_6_1"
_SCREAMING_SNAKE_CASE     :					Union[str, Any]      =       "sshleifer/tiny-mbart"

@require_torch
class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self     ,   __snake_case=False     ,   __snake_case=None     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=True     ,   ):
         snake_case											=							self.run_trainer(
             eval_steps=1     ,   max_len=1_2     ,   model_name=__snake_case     ,   num_train_epochs=1     ,   distributed=__snake_case     ,   extra_args_str=__snake_case     ,   predict_with_generate=__snake_case     ,   do_train=__snake_case     ,   do_eval=__snake_case     ,   do_predict=__snake_case     ,   )
         snake_case											=							TrainerState.load_from_json(os.path.join(__snake_case     ,   '''trainer_state.json'''       )       ).log_history

         if not do_eval:
              return

         snake_case											=							[log for log in logs if '''eval_loss''' in log.keys()]

         snake_case											=							eval_metrics[0]
         if predict_with_generate:
              assert "eval_bleu" in first_step_stats

              snake_case											=							eval_metrics[-1]
              assert isinstance(last_step_stats['''eval_bleu''']     ,   __snake_case       )
              assert not math.isnan(float(last_step_stats['''eval_loss''']       )       ), "eval_loss must not be `nan`"
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         self.run_seqaseq_quick()
    @require_torch_multi_gpu
    def 						a_     (			self       ):
         self.run_seqaseq_quick(distributed=__snake_case       )
    @require_torch_multi_gpu
    def 						a_     (			self       ):
         self.run_seqaseq_quick(distributed=__snake_case       )
    @unittest.skip('''Requires an update of the env running those tests'''       )
    @require_torch_multi_gpu
    @require_fairscale
    def 						a_     (			self       ):
         self.run_seqaseq_quick(distributed=__snake_case     ,   extra_args_str='''--sharded_ddp simple'''       )
    @unittest.skip('''Requires an update of the env running those tests'''       )
    @require_torch_multi_gpu
    @require_fairscale
    def 						a_     (			self       ):
         self.run_seqaseq_quick(distributed=__snake_case     ,   extra_args_str='''--sharded_ddp simple --fp16'''       )
    @unittest.skip('''Requires an update of the env running those tests'''       )
    @require_torch_multi_gpu
    @require_fairscale
    def 						a_     (			self       ):
         self.run_seqaseq_quick(distributed=__snake_case     ,   extra_args_str='''--sharded_ddp zero_dp_2'''     ,   predict_with_generate=__snake_case       )
    @unittest.skip('''Requires an update of the env running those tests'''       )
    @require_torch_multi_gpu
    @require_fairscale
    def 						a_     (			self       ):
         self.run_seqaseq_quick(
             distributed=__snake_case     ,   extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''     ,   predict_with_generate=__snake_case       )
    @require_apex
    @require_torch_gpu
    def 						a_     (			self       ):
         # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
         # program and it breaks other tests that run from the same pytest worker, therefore until this is
         # sorted out it must be run only in an external program, that is distributed=True in this
         # test and only under one or more gpus - if we want cpu will need to make a special test
         #
         # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
         # 2nd main() call it botches the future eval.
         #
         self.run_seqaseq_quick(distributed=__snake_case     ,   extra_args_str='''--fp16 --fp16_backend=apex'''       )
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=__snake_case     ,   extra_args_str='''--fp16 --fp16_backend=apex'''       )
    @parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']       )
    @require_torch_multi_gpu
    def 						a_     (			self     ,   __snake_case       ):
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         snake_case											=							{
             # test with the default log_level - should be info and thus log info once
             '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
         }

         snake_case											=							experiments[experiment_id]
         snake_case											=							{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
         snake_case											=							'''Running training'''
         with CaptureStderr() as cl:
              self.run_seqaseq_quick(**__snake_case     ,   extra_args_str=data['''extra_args_str''']       )
         snake_case											=							len(re.findall(__snake_case     ,   cl.err       )       )
         self.assertEqual(__snake_case     ,   data['''n_matches''']       )
    @slow
    def 						a_     (			self       ):
         snake_case											=							self.run_trainer(
             eval_steps=2     ,   max_len=1_2_8     ,   model_name=__snake_case     ,   learning_rate=3E-4     ,   num_train_epochs=1_0     ,   distributed=__snake_case     ,   )

         # Check metrics
         snake_case											=							TrainerState.load_from_json(os.path.join(__snake_case     ,   '''trainer_state.json'''       )       ).log_history
         snake_case											=							[log for log in logs if '''eval_loss''' in log.keys()]
         snake_case											=							eval_metrics[0]
         snake_case											=							eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats['''eval_bleu''']     ,   __snake_case       )

         # test if do_predict saves generations and metrics
         snake_case											=							os.listdir(__snake_case       )
         snake_case											=							{os.path.basename(__snake_case       ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents
    @slow
    @require_bitsandbytes
    def 						a_     (			self       ):
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(__snake_case       ) -> Tuple[int, float]:
              snake_case											=							'''--skip_memory_metrics 0'''

              snake_case											=							self.run_trainer(
                  max_len=1_2_8     ,   model_name=__snake_case     ,   learning_rate=3E-4     ,   num_train_epochs=1     ,   optim=__snake_case     ,   distributed=__snake_case     ,   extra_args_str=__snake_case     ,   do_eval=__snake_case     ,   do_predict=__snake_case     ,   n_gpus_to_use=1     ,   )

              # Check metrics
              snake_case											=							TrainerState.load_from_json(Path(__snake_case     ,   '''trainer_state.json'''       )       ).log_history
              snake_case											=							int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0       )
              snake_case											=							int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0       )

              snake_case											=							logs[0]['''train_loss''']
              return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         snake_case					,    snake_case					,    snake_case											=							train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value       )
         snake_case					,    snake_case					,    snake_case											=							train_and_return_metrics(OptimizerNames.ADAMW_BNB.value       )

         snake_case											=							gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         snake_case											=							gpu_peak_mem_orig + gpu_alloc_mem_orig
         snake_case											=							gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         snake_case											=							gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         snake_case											=							1_2_0

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             __snake_case     ,   __snake_case     ,   '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
             F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
             F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''     ,   )

         self.assertGreater(
             __snake_case     ,   __snake_case     ,   '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
             F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
             F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''     ,   )

         self.assertEqual(
             __snake_case     ,   __snake_case     ,   F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''       )




    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case = 3E-3     ,   __snake_case = "adafactor"     ,   __snake_case = False     ,   __snake_case = None     ,   __snake_case = 0     ,   __snake_case = True     ,   __snake_case = True     ,   __snake_case = True     ,   __snake_case = True     ,   __snake_case = None     ,   ):
         snake_case											=							self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         snake_case											=							self.get_auto_remove_tmp_dir()
         snake_case											=							F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(__snake_case       )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__snake_case       )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

         snake_case											=							F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(__snake_case       )}
        '''.split()

         snake_case											=							'''
            --do_predict
        '''.split()

         snake_case											=							[]
         if do_train:
              args += args_train

         if do_eval:
              args += args_eval

         if do_predict:
              args += args_predict

         if predict_with_generate:
              args += "--predict_with_generate".split()

         if do_train:
              if optim == "adafactor":
                   args += "--adafactor".split()
              else:
                   args += F'''--optim {optim}'''.split()

         if extra_args_str is not None:
              args += extra_args_str.split()

         if distributed:
              if n_gpus_to_use is None:
                   snake_case											=							get_gpu_count()
              snake_case											=							get_torch_dist_unique_port()
              snake_case											=							F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
              snake_case											=							[sys.executable] + distributed_args + args
              # keep for quick debug
              # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
              execute_subprocess_async(__snake_case     ,   env=self.get_env()       )
         else:
              snake_case											=							['''run_translation.py'''] + args
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   main()

         return output_dir








import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetrImageProcessor

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=7     ,   __snake_case=3     ,   __snake_case=3_0     ,   __snake_case=4_0_0     ,   __snake_case=True     ,   __snake_case=None     ,   __snake_case=True     ,   __snake_case=1 / 2_5_5     ,   __snake_case=True     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=[0.5, 0.5, 0.5]     ,   __snake_case=True     ,   ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         snake_case											=							size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							num_channels
         snake_case											=							min_resolution
         snake_case											=							max_resolution
         snake_case											=							do_resize
         snake_case											=							size
         snake_case											=							do_rescale
         snake_case											=							rescale_factor
         snake_case											=							do_normalize
         snake_case											=							image_mean
         snake_case											=							image_std
         snake_case											=							do_pad
    def 						a_     (			self       ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_pad": self.do_pad,
         }




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False       ):
         if not batched:
              snake_case											=							image_inputs[0]
              if isinstance(__snake_case     ,   Image.Image       ):
                   snake_case					,    snake_case											=							image.size
              else:
                   snake_case					,    snake_case											=							image.shape[1], image.shape[2]
              if w < h:
                   snake_case											=							int(self.size['''shortest_edge'''] * h / w       )
                   snake_case											=							self.size['''shortest_edge''']
              elif w > h:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							int(self.size['''shortest_edge'''] * w / h       )
              else:
                   snake_case											=							self.size['''shortest_edge''']
                   snake_case											=							self.size['''shortest_edge''']

         else:
              snake_case											=							[]
              for image in image_inputs:
                   snake_case					,    snake_case											=							self.get_expected_values([image]       )
                   expected_values.append((expected_height, expected_width)       )
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[0]       )[0]
              snake_case											=							max(__snake_case     ,   key=lambda __snake_case       : item[1]       )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						DetrImageProcessor if is_vision_available() else None
    def 						a_     (			self       ):
         snake_case											=							DetrImageProcessingTester(self       )
    @property
    def 						a_     (			self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_mean'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''image_std'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_normalize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_rescale'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''rescale_factor'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_resize'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''size'''       )       )
         self.assertTrue(hasattr(__snake_case     ,   '''do_pad'''       )       )
    def 						a_     (			self       ):
         snake_case											=							self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )

         snake_case											=							self.image_processing_class.from_dict(
             self.image_processor_dict     ,   size=4_2     ,   max_size=8_4     ,   pad_and_return_pixel_mask=__snake_case       )
         self.assertEqual(image_processor.size     ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}       )
         self.assertEqual(image_processor.do_pad     ,   __snake_case       )
    def 						a_     (			self       ):
         pass
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   Image.Image       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values
         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   numpify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   np.ndarray       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    def 						a_     (			self       ):
         # Initialize image_processing
         snake_case											=							self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         snake_case											=							prepare_image_inputs(self.image_processor_tester     ,   equal_resolution=__snake_case     ,   torchify=__snake_case       )
         for image in image_inputs:
              self.assertIsInstance(__snake_case     ,   torch.Tensor       )

         # Test not batched input
         snake_case											=							image_processing(image_inputs[0]     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,   )

         # Test batched
         snake_case											=							image_processing(__snake_case     ,   return_tensors='''pt'''       ).pixel_values

         snake_case					,    snake_case											=							self.image_processor_tester.get_expected_values(__snake_case     ,   batched=__snake_case       )

         self.assertEqual(
             encoded_images.shape     ,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )     ,   )
    @slow
    def 						a_     (			self       ):
         # prepare image and target
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''image_id''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




    @slow
    def 						a_     (			self       ):
         # prepare image, target and masks_path
         snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''     ,   '''r'''       ) as f:
              snake_case											=							json.loads(f.read()       )

         snake_case											=							{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

         snake_case											=							pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''       )

         # encode them
         snake_case											=							DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''       )
         snake_case											=							image_processing(images=__snake_case     ,   annotations=__snake_case     ,   masks_path=__snake_case     ,   return_tensors='''pt'''       )

         # verify pixel values
         snake_case											=							torch.Size([1, 3, 8_0_0, 1_0_6_6]       )
         self.assertEqual(encoding['''pixel_values'''].shape     ,   __snake_case       )

         snake_case											=							torch.tensor([0.2796, 0.3138, 0.3481]       )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify area
         snake_case											=							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']     ,   __snake_case       )       )
         # verify boxes
         snake_case											=							torch.Size([6, 4]       )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape     ,   __snake_case       )
         snake_case											=							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]     ,   __snake_case     ,   atol=1E-3       )       )
         # verify image_id
         snake_case											=							torch.tensor([3_9_7_6_9]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']     ,   __snake_case       )       )
         # verify is_crowd
         snake_case											=							torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']     ,   __snake_case       )       )
         # verify class_labels
         snake_case											=							torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']     ,   __snake_case       )       )
         # verify masks
         snake_case											=							8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()     ,   __snake_case       )
         # verify orig_size
         snake_case											=							torch.tensor([4_8_0, 6_4_0]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']     ,   __snake_case       )       )
         # verify size
         snake_case											=							torch.tensor([8_0_0, 1_0_6_6]       )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']     ,   __snake_case       )       )




